Evidence map›Paper›PMID 41717849›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

vEMRec: High-Resolution Volume Electron Microscopy Reconstruction Based on Structure-Preserving and High-Fidelity 3D Alignment.

Zhenbang Zhang, Hongjia Li, Zhongjun Yang, Zhiqiang Xu, Duanchen Sun, Xin Gao, Fa Zhang, Renmin Han

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhenbang ZhangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0009-0003-0512-3931
Hongjia LiSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.
Zhongjun YangQilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China.
Zhiqiang XuDepartment of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, UAE.
Duanchen SunSchool of Mathematics, Shandong University, Jinan, China.
Xin GaoComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering(CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.ORCID https://orcid.org/0000-0002-7108-3574
Fa ZhangSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0003-1939-6677
Renmin HanResearch Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, China.ORCID https://orcid.org/0000-0003-4761-6526

Funding

Center of Excellence for Smart Health (KCSH) 5932Center of Excellence on Generative AI 5940Dubai Future Foundation 2024CANAD-MES-061King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) FCC/1/5932-12-11King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) FCC/1/5940-20-03King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) REI/1/5289-01-01King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) REI/1/5992-01-01King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) URF/1/6599-01-02King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) URF/1/6713-01-01National Key Research and Development Program of China 2021YFF0704300Natural Science Foundation of China Projects Grant W2511070Natural Science Foundation of Shandong Province ZR2023YQ057
6 · The paper itself

Abstract

Three-dimensional (3D) alignment is a key step in volume electron microscopy (vEM), aimed at addressing misalignment during data acquisition, thereby recovering the correct biological structures. However, automated 3D alignment has long been challenged by the dilemma between eliminating nonlinear distortions and capturing natural morphological variations inherent to biological specimens. Here, we present vEMRec, a paradigm-shifting, fully automated algorithm for vEM 3D alignment. vEMRec redefines the 3D alignment problem by decoupling it into high-frequency and low-frequency subproblems. In this framework, precision rigid alignment is applied to correct rigid distortions, while a Gaussian filter-driven elastic registration algorithm addresses nonlinear distortions, all the while faithfully preserving biologically plausible deformations. Extensive experiments demonstrate that vEMRec achieves a paradigm shift in 3D alignment. Serving as a critical preprocessing step, vEMRec enhances performance in downstream isotropic reconstruction and 3D segmentation tasks by improving axial continuity in anisotropic data while preserving the structural integrity of ultrastructural details. Moreover, vEMRec accomplishes this through efficient computation, enabling TB-scale specimen analysis with biologically relevant throughput. vEMRec successfully optimized six representative large-scale real-world datasets, demonstrating its applicability, accuracy, and robustness for large-scale data processing.

Indexed as

deep learningseries section electron microscopyvolume electron microscopy

Identifiers

PMID41717849
PMCPMC13104135

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.